Machine learning methodology is primarily concerned with designing appropriate models/algorithms for datasets and problems, plus the capacity to learn the model parameters given data (made more complex with “big data”). Machine-learning has a broad range of applications, from making improved diagnoses in health care to tailoring products and ads to individual customers. With increasing access to massive datasets, and to significant advances in computing resources, the quality of machine learning performance (e.g., prediction accuracy) has improved markedly.

This program will concentrate on methods that allow machine-learning algorithms to learn effectively on large datasets. Throughout the course, you’ll:

  • Understand and leverage deep machine learning
  • Discuss the latest methods for image and video analysis, natural language processing, reinforcement learning, and data synthesis/modeling
  • Explore the mathematical and statistical principles that lie at the heart of machine learning
  • Receive hands-on training with software using the Google TensorFlow platform

Who Should Attend?

This program is most appropriate for individuals interested in learning about machine learning, with a focus on recent algorithms, like deep learning. You’ll learn the mathematics and statistics at the foundation of modern machine learning and get hands-on training in the latest machine learning software, using Google TensorFlow platform.

You should have a strong background in computing (e.g., Python, Matlab, SAS, etc.; any modern computing language), to be capable of learning how to use and apply modern machine learning software. For participants who also have a strong mathematical and statistical background (strength in calculus and in basic statistics, at the senior undergraduate level), the opportunity to understand the fundamentals of machine learning will be available. Strength in mathematics and statistics is a significant plus; however, it is not required to benefit from the hands-on software portion of the program.

More about this program

Day 1

Lecturer: David Carlson

Content: Basic concepts in machine learning

  • Introduction to model building
  • Scaling to “big data” with stochastic gradient descent
  • Backpropagation as an efficient computation method

Day 2

Lecturer: Tim Dunn

Content: Deep convolutional neural networks

  • Image analysis
  • Image segmentation, object detection and object localization

Day 3

Lecturer: Lawrence Carin

Content: Reinforcement Learning

  • Basic concepts for optimal policies in complex environments
  • Q-learning and leveraging deep networks
  • Applications of reinforcement learnings

Day 4

Lecturer: Ricardo Henao

Content: Data synthesis, with an emphasis on images

  • Generative adversarial network (GAN)
  • Deep networks for GAN
  • Learning and applications of GAN

Day 5

Lecturer: Mohit Bansal

Content: Methods for natural language processing

  • Word embeddings
  • Recurrent neural networks
  • Temporal convolutional neural networks


This five-day program will offer lectures on the mathematics and statistics at the heart of machine learning, plus hands-on training about implementing machine learning tools with the TensorFlow software platform. Each day, material will be discussed at three levels. First, concepts will be presented in an intuitive manner, with light emphasis on the mathematical details. The second portion of each day will then examine the underlying mathematics and statistics of the machine learning algorithms in greater detail. The third portion of each day will focus on software implementation in TensorFlow. Finally, breakout sessions for reviews will be presented in the final hour each day, and there will also be special-topic lectures in the last hour; each student may select from among the parallel activities in the final hour.


Each day will be arranged as follows:

9:00-10:15 a.m.  

Lecture 1: Mathematically-light introduction to the focus of the day

10:45 a.m.-noon       

Lecture 2: Mathematically rigorous discussion of the focus of the day

1:30-3:30 p.m.    

Software discussion and hands-on training with TensorFlow

4:00-5:00 p.m.          

Three parallel activities will take place in the last hour each day, and each student may select what is most appropriate for them: (1) In the main lecture hall, there will be a special-topic presentation on an important area of machine learning; (2) there will be a dedicated breakout session for attendees who are medical professionals, to place the earlier lectures in the context of healthcare (Matthew Englehard, MD/PhD, will lead these reviews); and (3) there will be breakout sessions for all other students, to review the earlier lectures that day (led by Lawrence Carin).

The agenda for the special-top lectures (4-5pm) is as follows:

Day 1: The concept of interpretable machine learning (lecturer: Cynthia Rudin)

Day 2: Interpretable machine learning in practice (lecturer: Cynthia Rudin)

Day 3: Applications of machine learning in vision (lecturer: Guillermo Sapiro)

Day 4: Machine learning for face recognition (lecturer: Guillermo Sapiro)

Day 5: Hardware implementations of machine learning (lecturer: Helen Li)


At the end of the program, you should be able to use TensorFlow to implement the latest machine learning methods for analyzing images, video, and natural language (text). For those with a strong mathematical background, the underlying methodology of machine learning will also be covered. You will be given assignments to test your knowledge of the material, so you can get a sense of how well you have absorbed these concepts. Breakout sessions will also be held to offer clarification on concepts and help with hands-on software implementations on provided example datasets.

Program Registration

 The Machine Learning Summer School program will be held:
June 25-29, 2018

Students: $300
Non-Students: $1000

Register Now

Contact Details

Duke University's Fuqua School of Business
100 Fuqua Drive
Durham, NC 27708-0120 USA
Tel +1.919.660.8011
Toll Free +1.800.372.3932

Office Hours
Monday - Friday
8:30 a.m. - 5:00 p.m.


Lawrence Carin

Lawrence Carin

Lawrence Carin is a professor of Electrical & Computer Engineering (ECE) at Duke, where he also serves as the Vice Provost for Research. He earned the BS, MS and PhD degrees from the University of Maryland in 1985, 1986 and 1989, respectively. He held the William H. Younger distinguished professorship at Duke from 2004-2014, and he has also served as the department chair of Duke’s ECE department. His research focuses on machine learning, with a recent emphasis on deep learning. He has authored over 350 peer-reviewed papers, and he is an IEEE Fellow. He was co-founder of Signal Innovations Group (acquired by BAE Systems in 2014), and is currently the Chief Scientist of the startup Infinia ML.  

Guillermo Sapiro

Guillermo Sapiro

Dr. Guillermo Sapiro is the Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering. He works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. Dr. Sapiro has authored and co-authored over 300 papers in these areas and has written a book published by Cambridge University Press, January 2001. He is a Fellow of IEEE and SIAM and was the founding editor-in-chief of the SIAM Journal on Imaging Sciences.

David Carlson

David Carlson

David Carlson is an assistant professor in the Department of Civil and Environmental Engineering and the Department of Biostatistics and Bioinformatics. He is also a member of the Duke Clinical Research Institute. He received his Ph.D., M.S., and B.S.E. in Electrical and Computer Engineering from Duke University.

Dr. Carlson's research is focused on how modern machine learning and statistical techniques can be used  for the analysis of large data sets, and the design of novel experiments to elucidate scientific understanding. He has developed algorithms and analysis methods for diverse engineering and health applications.

Ricardo Henao

Ricardo Henao

Ricardo Henao is an assistant professor in the Department of Biostatistics and Bioinformatics at Duke University. He is also associated with the Information Initiative at Duke (iiD), the Center for Applied Genomics and Precision Medicine (CAGPM), the Center for Health Data Science and the Duke Clinical Research Institute (DCRI), all at Duke. Dr. Henao's recent research has been focused on the development of sophisticated machine learning models, including deep learning approaches, for the analysis and interpretation of clinical and biological data with applications to predictive modeling for diverse clinical outcomes.

Cynthia Rudin

Cynthia Rudin

Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, statistical science and mathematics at Duke University and directs the Prediction Analysis Lab. Dr. Rudin is a recipient of the INFORMS Innovative Applications in Analytics Awards, an NSF CAREER award. She was named one of the "Top 40 Under 40" by Poets and Quants, and was named one of the 12 most impressive professors at MIT by . She is past chair of the INFORMS Data Mining Section, and the current chair of the Statistical Learning and Data Science section of the American Statistical Association. 

Timothy Dunn

Timothy Dunn

Timothy Dunn earned a BA in Molecular and Cell Biology from UC Berkeley in 2008 and a PhD in Neurobiology from Harvard University in 2015. At Harvard, Dr. Dunn built models to explain animal behavior and used artificial neural networks to analyze large brain imaging datasets.

As an independent Harvard College Fellow, he continued his computational research and lectured on modern methods for biological data analysis. He is now working with the Duke Forge to apply these methods to pressing problems in the health and biomedical sciences.

Mohit Bansal

Mohit Bansal

Dr. Mohit Bansal is an assistant professor in the Computer Science department at UNC Chapel Hill. Prior to this, he was a research assistant professor at TTI-Chicago. He received his PhD from UC Berkeley in 2013 and his BTech from IIT Kanpur in 2008. His research interests are in statistical natural language processing and machine learning, with a particular interest in multimodal, grounded, and embodied semantics (i.e., language with vision and speech, for robotics), human-like language generation and Q&A/dialogue, and interpretable and structured deep learning. He is a recipient of the 2017 DARPA Young Faculty Award, 2017 ACL Outstanding Paper Award, 2014 ACL Best Paper Award Honorable Mention, 2016 and 2014 Google Faculty Research Awards, 2016 Bloomberg Data Science Award, and 2014 IBM Faculty Award.

Matthew Engelhard

Matthew Engelhard

Matthew Engelhard is the C. Keith Conners Fellow in Digital Health in the Duke University Department of Psychiatry and Behavioral Sciences. He is also affiliated with the Department of Electrical and Computer Engineering, the Center for Applied Genomics and Precision Medicine, and the Global Digital Health Science Center at Duke. Dr. Engelhard's research focuses on personalized machine learning models for ADHD management and smoking cessation, with emphasis on data collected with mobile devices and wearables. He received his MD and PhD in Systems and Information Engineering from the University of Virginia, and his BSE in Electrical and Biomedical Engineering and Mathematics from Duke University. During the Machine Learning Summer School, Dr. Engelhard will be providing review sessions explicitly targeted to medical professions, including medical doctors.

Hai Li

Hai “Helen” Li

Hai “Helen” Li is currently Clare Boothe Luce Associate Professor of Electrical and Computer Engineering Department at Duke University. She works on hardware/software co-design for accelerating machine learning, brain-inspired computing systems, and memory architecture and optimization. She has authored or co-authored more than 200 technical papers in these areas and has written a book published by CRC Press. Dr. Li is a recipient of the NSF CAREER Award (2012), the DARPA Young Faculty Award (2013), TUM-IAS Hans Fisher Fellowship from Germany (2017) and seven best paper awards. Dr. Li is a senior member of IEEE and a distinguished member of ACM, a distinguished speaker of ACM (2017-2020), and a distinguished lecture of IEEE CAS society (2018-2019).

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